Hotspot Avoidance Method for Manufacturing Integrated Circuits

ABSTRACT

A method includes cropping a plurality of images from a layout of an integrated circuit, generating a first plurality of hash values, each from one of the plurality of images, loading a second plurality of hash values stored in a hotspot library, and comparing each of the first plurality of hash values with each of the second plurality of hash values. The step of comparing includes calculating a similarity value between the each of the first plurality of hash values and the each of the second plurality of hash values. The method further includes comparing the similarity value with a pre-determined threshold similarity value, and in response to a result that the similarity value is greater than the pre-determined threshold similarity value, recording a position of a corresponding image that has the result. The position is the position of the corresponding image in the layout.

BACKGROUND

In the manufacturing of integrated circuits, process-related defectssuch as topology hotspots are found after the respective process iscompleted by physically measuring from the manufactured wafers. Forexample, to find the defects related to the Chemical Mechanical Polish(CMP), several phases including circuit design phase, circuit layoutphase, manufacturing and performing CMP on the physical wafers, andmeasuring the physical wafers have to be performed to find topologydefects. This process typically takes three months.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIGS. 1 through 4 illustrate the cross-sectional views of structureswith chemical mechanical polish process performed thereon and theresults in accordance with some embodiments.

FIG. 5 illustrates a schematic flow in the design and manufacturing ofan integrated circuit in accordance with some embodiments.

FIG. 6 illustrates a process flow for constructing a hotspot library inaccordance with some embodiments.

FIG. 7 illustrates example images and the generated hash values inaccordance with some embodiments.

FIG. 8 illustrates a schematic view of an example wafer having hotspotsin accordance with some embodiments.

FIG. 9 illustrates an example cropped image in accordance with someembodiments.

FIG. 10 illustrates the grouping of hash values in accordance with someembodiments.

FIGS. 11 and 12 illustrate the represented regions in a cropped imagewith different pattern densities and line widths in accordance with someembodiments.

FIG. 13 illustrates a process flow of using the hotspot library todetermine likely hotspots in accordance with some embodiments.

FIG. 14 illustrates the cropping of a layout into a plurality of croppedimages in accordance with some embodiments.

FIG. 15 illustrates a process flow for finding a recipe corresponding tohotspots in accordance with some embodiments.

FIG. 16 illustrates a graphical representation in the finding of arecipe in accordance with some embodiments.

FIG. 17 illustrates an example recipe in accordance with someembodiments.

FIG. 18 illustrates a process of improving a hotspot prevention modeland improving recipes in accordance with some embodiments.

FIG. 19 illustrates a system for performing the tasks in accordance withsome embodiments.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the invention. Specificexamples of components and arrangements are described below to simplifythe present disclosure. These are, of course, merely examples and arenot intended to be limiting. For example, the formation of a firstfeature over or on a second feature in the description that follows mayinclude embodiments in which the first and second features are formed indirect contact, and may also include embodiments in which additionalfeatures may be formed between the first and second features, such thatthe first and second features may not be in direct contact. In addition,the present disclosure may repeat reference numerals and/or letters inthe various examples. This repetition is for the purpose of simplicityand clarity and does not in itself dictate a relationship between thevarious embodiments and/or configurations discussed.

Further, spatially relative terms, such as “underlying,” “below,”“lower,” “overlying,” “upper” and the like, may be used herein for easeof description to describe one element or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. The apparatus may be otherwiseoriented (rotated 90 degrees or at other orientations) and the spatiallyrelative descriptors used herein may likewise be interpretedaccordingly.

A hotspot avoidance method for manufacturing integrated circuits isprovided in accordance with some embodiments. The system and theprocesses for predicting the hotspots and using the predicted hotspotsto find an optimum recipe are illustrated in accordance with someembodiments. Some variations of some embodiments are discussed.Embodiments discussed herein are to provide examples to enable making orusing the subject matter of this disclosure, and a person havingordinary skill in the art will readily understand modifications that canbe made while remaining within contemplated scopes of differentembodiments. Throughout the various views and illustrative embodiments,like reference numbers are used to designate like elements. Althoughmethod embodiments may be discussed as being performed in a particularorder, other method embodiments may be performed in any logical order.

Throughout the description, the term “hotspot” refers to the defectsgenerated in the integrated circuit manufacturing processes rather thanthe defects related to the design. Alternatively stated, the term“hotspot” refers to the process-related defects. An example of thehotspots is the defects generated in the Chemical Mechanical Polish(CMP) processes, as will be discussed in detail referring to FIGS. 1through 4, while the hotspots may also refer to other types of defectsincluding, and not limited to, the defects relating to etching processes(for example, the parts that are intended to be removed but failed to beremoved in etching), the defects related to deposition process, etc.

FIGS. 1 through 4 illustrate the deposition of some features, the CMPprocess, and several possible results as a result of the CMP. It isappreciated that FIGS. 1 through 4 illustrate the example structure of aCMP process for the formation of metal lines, while an actual CMPprocess may be applied on different structures, different material, andthe like. Referring to FIG. 1, wafer 10 is provided. Wafer 10 includesbase layer 20, which may include, for example, a silicon substrate andthe overlying structures and layers, and the details are notillustrated. A plurality of trenches may be formed extending into adielectric layer of the base layer 20. Deposition processes are thenmade to deposit glue layer 22, and a metallic material 24 over gluelayer 22. In accordance with some embodiments of the present disclosure,glue layer 22 may be formed of or comprise titanium, titanium nitride,tantalum, tantalum nitride, or the like. The filling material 24 mayinclude copper, aluminum copper, or the like. Due to the topology of thetrenches in base layer 20, the deposited metallic material 24 hasnon-planar top surfaces that may reflect the topology of the base layer20. A CMP process is performed to remove excess portions of metallicmaterial 24, resulting in a plurality of conductive features 26(including 26A and 26B), which may include metal lines, vias, contactplugs, or the like, as shown in FIG. 2, 3, or 4.

Due to various factors such as the topology of base structure 20, forexample, the density and the widths of the trenches, and the recipe ofthe CMP process, different results may be achieved, as shown in FIGS. 2,3, and 4. Throughout the description, the term “recipe” refers to thecollection of the process conditions such as the number of (sub) stepsin the CMP process, the types of slurry, the flow rates of the slurry,the down force of the wafer against the polishing pad, the dressing, therotation speed, etc. A recipe thus includes a fixed collection ofprocess conditions. When one or more of the process conditions of arecipe is changed, it is considered as that another recipe is generated.FIG. 2 illustrates an ideal case to be achieved. In FIG. 2, the topsurfaces of all resulting conductive features 26 (including 26A and 26B)are coplanar, regardless of the widths and the pattern densities ofconductive features 26.

FIG. 3 illustrates an actual case, which is non-ideal but is stillacceptable. Due to the pattern-loading effect, the portions of metallicmaterial 24 with higher density and/or larger widths are polished morethan the portions of metallic material 24 with lower density and/orsmaller widths, resulting in dishing effect, with recesses generated.The depths D1 of the recesses are smaller than design specification, andhence no hotspot is generated. For example, the design specification mayrequire the dishing depth to be smaller than about 10 nm. Since depthsD1 of all recesses are smaller than the specification, the result isacceptable, and the recesses are not hotspots.

FIG. 4 illustrates a case in which hotspots are generated where trenchesare wide and/or the pattern densities of trenches are high. For example,the recessing depths D2 of the wide trenches are greater than the designspecification (10 nm, for example). These out-of-specification recessesmay cause problems for subsequent processes, which problems may includecircuit shorting, circuit breaking, or the like, depending on thespecific circuit design. Throughout the description, theout-of-specification recesses are used as the example hotspots toexplain the concept of the present disclosure. Also, it is appreciatedthat FIGS. 3 and 4 illustrate the over-polishing in the CMP process,while under-polishing, in which some portions are polished less (andthus higher than the top surfaces of base layer 20) may also occur, andhotspots may also be generated when the resulting humps areout-of-specification. The hotspots may result in the loss of productionyield, and need to be eliminated or at least reduced to be withinspecification.

FIG. 5 illustrates a schematic flow in the design and manufacturing ofan integrated circuit in accordance with some embodiments of the presentdisclosure. A circuit design is first provided (process 30), and thedesign may include the schematic of the circuit. Next, the layout of thecircuit is prepared (process 32). From the layout, the hotspots andtheir positions in the layout are predicted (process 34) using a modelprovided in accordance with the embodiments of the present disclosure.The details of the prediction of the hotspots are shown as process 300in FIG. 13. The generation the using, and the improvement of the modelare discussed in detail in subsequent paragraphs. Throughout thedescription, the model is referred to as a hotspot prevention model.

After the hotspots are predicted, a recipe (referred to as a selectedrecipe hereinafter) that may result in the least number of hotspots isselected (process 36) based on the predicted hotspots, so that by usingthe selected recipe to perform the CMP, the number of hotspot isminimized, and the severity (such as the depths D1 and D2 as shown inFIG. 4) is minimized. The details of the selection of the recipe areshown as process 400 in FIG. 15. The recipe is then used for themanufacturing of the circuit on wafers, and is used to perform a CMPprocess (process 38) on physical wafers. It is appreciated that up tothe time point the selected recipe is chosen, there may not be any CMPprocess performed on any wafer that implements this specific layout.After the CMP process, the resulting polished wafers may be tested(process 40) to verify the occurrence and the positions of hotspots. Thetest results may also be used to improve the hotspot prevention model,which improvement process is included in the process 500 shown in FIG.18.

In subsequent paragraphs, a process flow 200 (FIG. 6) for constructingand improving a hotspot library, a process flow 300 (FIG. 13) of usingthe hotspot library to predict hotspots on a circuit layout, and aprocess flow 400 (FIG. 15) for suggesting selected recipes are discussedin detail. These processes in combination provide a solution forpredicting and eliminating (or at least minimizing) hotspots withoutactually performing processes (such as CMP processes) on physicalwafers.

Referring to FIG. 6, process flow 200 for generating and improving ahotspot library is provided. Referring to process 202, a training layoutof a chip implementing a circuit is provided. The chip layout may be inthe form of Graphic Data System (GDS) format, or any other applicableformats. Throughout the description, layouts are alternatively referredto as GDS files. It is appreciated that the training layout may be usedspecifically for generating the hotspot library, and is not used in themass production of products, or may be a production layout that will beimplemented on production wafers.

An experimental wafer is then manufactured to implement the trainingGDS. FIG. 8 illustrates a schematic view of the corresponding wafer 42,which includes a plurality of chips 44, with the layout (the trainingGDS) implemented in each of chips 44. After a CMP process is performed,a test is performed to measure the surface topography of wafer 42(process 204 in FIG. 6), and hotspots 46 in wafer 42 are identified. Thepositions of hotspots 46 in wafer 42 are recorded, as shown as process208 in FIG. 6. Since there may be a plurality of hotspots 46 found inprocess 204, a plurality of positions in the wafer 42 are recorded.

Next, as shown as process 210 in FIG. 3, for each of the found hotspots46, an image is cropped from the layout, which may be in the form of therow GDS file. For example, FIG. 9 illustrates an example cropped image.Assuming a hotspot 46 is found at position 48, the image surroundinghotspot 46 is cropped from the row layout data. The image may berectangular, and may be a square. The length L1 and width W1 of thecropped image is selected so that the surrounding environment aroundhotspot 46 is large enough to include the surrounding features whosepatterns and density may result in the hotspot 46, but not too large toinclude features not affecting the generation of the hotspot 46. Forexample, length L1 and width W1 may be in the range between about 64 μmand about 512 μm. Since there may be a single or a plurality of hotspots46 found in the process 204, a single or a plurality of images may becropped from the layout of wafer 42.

Referring to process 212 in FIG. 6, hash values are generated from thecropped images. FIG. 7 illustrates an example for generating hash valuesfrom images. Since images cannot be indexed and cannot be searched, animage is represented by a hash value, which is a unique digitalrepresentation of the image. The hash values and the images areone-to-one correspondent, so that identical images will generateidentical hash values, and different images will generate different hashvalues. Furthermore, images that are similar to each other will generatesimilar hash values, and the similarity of hash values may becalculated. The similarity of hash values also represents the similarityof images. For example, the similarity of hash values may range between0 and 1, with value 0 meaning the images are totally different from eachother, and value 1 meaning the images are identical. The generation ofthe hash values from images, and the calculation of the similarity ofthe hash values may be performed using existing algorithms and tools.For example, Discrete Cosine Transform (DCT) algorithm used byPerceptual hash (pHash) is a known available algorithm.

The hash values may be obtained through intermediate values representedby two-dimensional matrices, which are then converted to the hash valuesrepresented by a series of digits and letters. For example, FIG. 7illustrates three example images image A, image B, and image C. Thedetails of the images are not shown. Image A shows a person wearingthick clothes sitting in snow, with a tree in the snow. Image B issimilar to image A, except it has been equalized from Image A, withcolors and contrast being adjusted. Image C shows a human face wearing agoggle on his forehead, with fire flames surrounding the face. On theright side of each of the images A, B, and C, a 8×8 two-dimensionalmatrix is provided, which matrix is generated from the correspondingimages and/or two-dimensional matrices. The hash values, which includedigits and letters, are shown on the right side of the respectivematrices. Referring back to FIG. 6, when a single or a plurality ofhotspots 46 are found in the process 204, a single or a plurality ofimages a plurality of images are cropped, and a single or a plurality ofhash values are generated in process 212.

Referring to process 214 in FIG. 6, the plurality of hash values aregrouped into one or a plurality of groups through a grouping algorithm,wherein the grouping is according to the similarity of hash values, withsimilar hash values grouped in a same hash group. An example groupingalgorithm is explained using FIG. 10. FIG. 10 illustrates a plurality ofcircles illustrated in a 2-dimensional space for visually illustratingthe hash values, with each circle representing a hash value generatedfrom a cropped image. In the grouping algorithm, a plurality of hashvalues, which include the illustrated hash values H1 through H13 as anexample, are processed one-by-one. It is assumed the order of processingis the sequence number of the hash values (1 to 13, for example). Whenhash value H1 is processed, since there is no other hash value, andthere is no hash group generated previously, a first hash group G1 isgenerated, and hash value H1 is placed into the first group G1. Thefirst placed hash value H1 is considered as being the center of thefirst group G1.

Next, as second hash value H2 is processed. A similarity value iscalculated between hash value H2 and the center of group G1, whichcenter is H1. Assuming the similarity value is greater than apredetermined threshold similarity value, it is considered that the hashvalues H1 and H2 are similar to each other, and hash value H2 belongs tohash group G1. Throughout the description, two hash values with thesimilarity value greater than the predetermined threshold similarityvalue are referred to as similar hash values. Their corresponding imagesare also referred to as similar images. Hash value H2 is added into hashgroup G1. In accordance with some embodiments, the threshold similarityvalue is 0.9, while other values may be used.

Assuming the next processed hash value is H3, a similarity value iscalculated between hash values H3 and the center H1 of hash group G1.Further assuming the similarity value is equal to or smaller than thepredetermined threshold similarity value, it is considered that the hashvalues H1 and H3 are not similar, and hash value H3 does not belong tohash group G1. Accordingly, a second hash group G2 will be generated,and hash value H3 is placed into hash group G2. Hash value H3 is thecenter of hash group G2.

In subsequent processes, each of the remaining hash values H4 throughH13 is processed one-by-one to calculate their similarity to the centersof the existing hash groups (such as G1 and G2), so that it can bedetermined to which hash group the newly processed hash values belongto, or whether new hash groups should be generated. FIG. 10 illustratesthe example in which hash value H12 is not similar to any of the centers(such as H1 and H3), so that an additional hash group G3 is generated,and hash value H12 is placed into hash group G1 are generated. Otherhash values H4-H11 and H13 are in hash groups G1 or G2.

Referring to process 216 in FIG. 6, the centers of each of the hashgroup is retrieved, which centers may be the hash values that are thefirst to be placed into each of the hash groups. After the centers ofthe hash groups are retrieved, the non-center hash values are discardedsince each of the center is similar to, and can represent, other hashvalues in its group. Alternatively stated, the cropped imagesrepresented by the discarded hash values in a same hash group aresimilar to the cropped image represented by the center hash value of thehash group. The hash values of the center hash values in different hashgroups are not similar to each other. Otherwise, if two center hashvalues are similar to each other, the two center hash values would havebeen placed in the same hash group, and as a result, only one of themwould have been the center, and the other would have been discarded.

Referring to FIG. 6, in process 218, a hotspot library (which includes adatabase) entry is composed for each of the hash values that are notdiscarded, which un-discarded hash values are the centers of the hashgroups. In accordance with some embodiments, a plurality of recipes aregenerated, as shown by process 206 in FIG. 6. The generation and theimprovement of the plurality of recipes are discussed referring to FIG.18. The plurality of recipes may also include empirical recipes that areknown to be able to eliminate hotspots for certain types of images. Eachof the plurality of recipes corresponds to a testing GDS as shown inFIG. 18, which has its hash value. The hash values of the un-discardedcenters of the hash groups are compared to (through calculation ofsimilarity values) the hash values of the GDS files corresponding to therecipes, and the recipes whose corresponding testing GDS are closest toa center hash value is associated to the respective center hash value.Each of the center hash values will be associated with a recipe.

In addition to the recipe, the cropped image, from which thecorresponding center hash value is generated, is associated with thecenter hash value. Also, as will be discussed in subsequent paragraphs,the expected topology information (such as whether the hotspot isunder-polish or over-polish, and the recessing depth or the hump height)is also associated with the recipe (as will be discussed referring toFIG. 18). The expected topology information is also obtained in theprocess shown in FIG. 18. Accordingly, each of the hotspot library entryincludes a hash value, the corresponding cropped image, thecorresponding recipe, and the corresponding topology information. Thereare a plurality of hotspot library entries generated. The indices of thehotspot library entries may be hash values. These hotspot libraryentries are saved in a database in hotspot library 222 as shown in theprocess 220 in FIG. 6.

As also shown in FIG. 6, through the processes in process flow 200, ahotspot prevention model 223 may be constructed and updated. The hotspotprevention model 223 incorporates the relationship between GDS files andhotspots as previously discussed, and uses GDS files or cropped images(or its corresponding hash values) as input parameters, and outputshotspots as output parameters.

FIGS. 11 and 12 schematically illustrate example cropped images that aresaved in the hotspot library along with the hash values. It isappreciated that FIGS. 11 and 12 are schematic, with the contours ofsome large regions 52 being shown, while the contours of some smallerregions are not shown. Furthermore, inside each of the regions, thereare a plurality of patterns such as parallel strips, and the patterns inthe illustrated regions 52 are not shown. The sizes, the shapes, and thedensity of the patterns in the illustrated regions 52 may be differentfrom each other. The different patterns, pattern densities, etc., whichform the environment surrounding the hot spot 46, are the reasons of thehotspot. For example, in FIG. 11, region 50 has small line widths, whichare much smaller than the line widths of its surrounding regions 52.Region 50 may also have a higher pattern density, which is much higherthan the pattern density of its surrounding regions 52. This causes thehotspot 46. When similar images with similar environments are found inother GDS file, it is expected hotspot may occur.

FIG. 12 illustrates a cropped image saved in hotspot library. Similarly,regions 52 illustrating the contours of some large regions are shown,while the contours of some smaller regions are not shown. Furthermore,inside each of the regions 52, there are a plurality of patterns such asparallel strips, which are not shown. The sizes, the shapes, and thedensity of the patterns in the illustrated regions 52 may be differentfrom each other, leading to the respective hotspot 46.

In the previous discussion, it is assumed that when process flow 200 isstarted, the hotspot library 222 has not been generated, and there is nohash group and center hash value generated previously. Accordingly, newhash groups will be generated and hotspot library will be generated fromscratch. Once hotspot library 222 is generated, the hotspot library 222may be improved ongoing using new training GDS files, which may be massproduction GDS files or the GDS files specifically for training purposeand not for production. The processes 202, 204, 208, 210, 212, and 214in FIG. 6 will be repeated on the new GDS files. Accordingly, aplurality of new hotspots are found from the newly manufactured waferimplementing the new GDS file, and hence a plurality of new images arecropped. A plurality of new hash values are then generated from thenewly cropped images. The newly generated hash values are then processedone-by-one, and their similarity to the existing center hash values(stored in hotspot library 222) are calculated. It is appreciated thatat this time, the stored center hash values are theoretically still thecenters of hash groups, except that each hash group is a single-membergroup (with non-center members being discarded already) having only onehash value left, which is the center of the previously processed hashgroup. The similarity to each of the stored center hash values inhotspot library 222 is calculated to determine whether the newlyprocessed hash value belongs to the existing hash groups or not. If itbelongs to one of the existing groups, the newly processed hash valueand its corresponding cropped image, recipe, topology information, etc.are discarded since the similar hotspot already exists in hotspotlibrary 222. If the newly processed hash value is not similar to any ofthe stored center hash values, the newly processed hash value and itscorresponding cropped image, recipe, topology information will be savedin the newly processed hash value as a new entry. Through this process,the hotspot library 222 may be improved.

FIG. 13 illustrates process flow 300, which is the process flow of usingthe hotspot library 222 to determine the likely hotspots in a new GDSfile (new layout). Referring to process 302, the new GDS file (layout)is provided. Next, in process 304, the new GDS file is cropped into aplurality of cropped images, each having the sizes L2×W2 as shown inFIG. 14. For example, FIG. 14 illustrates the design of layout 55, whichis divided into an array of images 56 with length L2 and width W2. Inaccordance with some embodiments, length L2 is equal to length L1 (FIG.8), and width W2 is equal to width W2. In accordance with otherembodiments, length L2 may be greater or smaller than length L1 (FIG.8), and width W2 may be greater or smaller than width W1.

Referring to process 306, each of the cropped images 56 is processed togenerate a hash value. The method of generating hash values is similarto what is discussed referring to process 212 in FIG. 6, and thus is notrepeated herein.

Referring to process 308 in FIG. 13, the (center) hash values saved inhotspot library 222 are loaded into a computer and the respectivesoftware. Each of the newly generated hash values is compared with eachof the loaded hash values from the hotspot library 222 to compare theirsimilarity, as shown in process 310. For example, when a newly generatedhash value and one of the hash values loaded from the hotspot libraryare similar, it is determined that the newly generated hash value andthe corresponding cropped image have already been represented by itssimilar hash value stored in hotspot library 222 (process 312). It isalso determined that hotspot(s) is likely to be generated in therespective cropped image. Accordingly, the position of the correspondingimage in the respective GDS file is marked (process 314). For example,when the cropped image is in row 2 and column 3 of the array dividedfrom layout 55 in FIG. 14, the position (2, 3) will be marked. Bycomparing all of the newly generated hash values (of the cropped images)with all of the hash values in the library, a list of all of thehotspots (if any) in the GDS 54 (FIG. 14) will be generated, and thecorresponding position of each of the hotspots is marked in thecorresponding GDS. The marked positions may be used in the future. Forexample, after a CMP process, the parts of the chips at the markedpositions are inspected to determine whether the hotspots have beensuccessfully eliminated or at least reduced.

On the other hand, if none of the newly generated hash values is similarto any of the hash values loaded from the hotspot library, it isdetermined that the newly generated hash values and the correspondingcropped image are not represented by any of the hotspot library entry inthe hotspot library 222 (process 312). Alternatively stated, no hashvalue is found from the GDS file, and the process may end (process 316).The layout may thus be manufactured without the concern of hotspots.

FIG. 15 illustrates process flow 400 for determining and suggestingrecipes, which recipes are used to perform the CMP processes in order toeliminate or at least reduce the hotspots found in process flow 300 asin FIG. 13. Referring to process 402, the list of the hotspots generatedin process 314 in process flow 300 (FIG. 13) is retrieved. If there wasno hotspot found, the process flow is ended. If one or more hotspot isfound, a comparison is made to find the hotspots in the hotspot library222 that are similar to the found hotspots. To perform the comparison,the hash values stored in the hotspot library 222 are first loaded intothe respective tool and computer, and the similarity values between thehash values of the found hotspots and the corresponding hash valuessaved in the hotspot library are calculated (by calculating similarityvalues), as shown in process 406. Depending on the total number of thefound hotspots in the GDS, there may be one or a plurality of similarityvalues, each corresponding to one of the found hotspots. The hotspots(their hash values) are ranked (process 406) according to theircorresponding similarity values, with the hotspots having highersimilarity values being higher ranked with higher priority than thehotspots having lower similarity values.

Referring to process 408 in process flow 400, the GDS files, thetopology information and the respective recipes corresponding to theranked hotspots are found from the hotspot library 222, for example, byindexing to the corresponding stored center hash values in hotspotlibrary 222. The topology information is analyzed, and one of the foundrecipes is selected (process 410). In accordance with some embodiments,the selected recipe is the recipe corresponding to the highest rankedhash value. In accordance with some embodiments, considering otherfactors, the selected recipe is the recipe corresponding to one of hashvalues that is not ranked highest. The selected recipe may thus be usedto perform the CMP process on the respective physical wafer.

FIG. 16 illustrates a graphical representation of the processes 406 and410 in the process flow 400 in FIG. 15. As is shown in FIG. 16, aplurality of hash values (represented by their two-dimensional matrices)of the found hotspots are generated, which correspond to process 404 inFIG. 15. Next, further according to process 406 in FIG. 15, thesimilarity values between the found hotspots and their correspondingrepresenting hotspots in the hotspot library 222 are calculated, and thefound hotspots (and their hash values) are ranked. As shown in FIG. 16,the order of the illustrated hash values is rearranged to show theranking. FIG. 16 also illustrates a plurality of recipes and GDS filescorresponding to the ranked hash values. Next, one of the recipes isselected (process 410), and the selected recipe is recipe B in thisexample. In other embodiments, the recipe with the highest ranking(recipe A) may be selected.

FIG. 17 illustrates an example recipe for a CMP process. Each recipe mayinclude (sub) steps performed in the CMP process, and the parameters tobe used in each of the steps. In the illustrated example, there are foursteps step1, step2, step3, and step4, each being performed with aplurality of parameters, with the parameters changed from step to step.For example, there may be head rotation A, head rotation B, slurry flowA (the flow rate of a first slurry), slurry flow B (the flow rate of asecond slurry), down force (of wafer head on polishing pad), dressingon/off (whether the pad conditioner is turned on or off), etc. TheX-axis indicates the time of the CMP process, and the Y-axis indicatesthe parameters and their corresponding values. For example, for each ofthe values, at any time, when the corresponding bar exists, thecorresponding parameter is turned on, and if the bar is wider (in theY-direction), the corresponding parameter has a higher value. Forexample, slurry B has a high flow rate in the initial stage of step1,and then is turned off during the rest of the time of step1. Slurry B isa relatively small flow rate during the entire sstep2, and is turned offduring the entire step3 and entire step4. The pad conditioner (indicatedby dressing on/off) is turned on with relatively small down force duringsteps step1 and step2, and turned on with relatively high down forceduring steps step3 and step4. Throughout the description, when a recipeis referred to as being adjusted, it indicates the adjustment of thesteps, parameters, and the values of the parameters in combination,which means when any of the parameters is adjusted, the recipe isconsidered as being adjusted.

FIG. 18 illustrates a process flow 500 for improving recipes and theprocess for training hotspot prevention model 223 (FIG. 6). Throughoutthe description, the hotspot prevention model 223 is alternativelyreferred to a Machine-Learning (ML) model since the model may beimproved through learning in the improvement process in process flow500. The improved recipes (recipes A, B, C, and D) may be used as thestored recipes in the process 206 in FIG. 6.

Referring to FIG. 18, a plurality of GDS (layouts) A, B, C, and D areprovided. The GDS A, B, C, and D may be identical to each other,slightly different from each other, or may be totally different fromeach other. Each of the recipe improvement processes goes throughiterations to improve the recipes. For example, a GDS A is provided(process 502), and is fed to hotspot prevention model 223 (FIG. 6), sothat hotspots that likely occur are generated and output by the hotspotprevention model 223. Recipe A is then suggested, and the generation ofthe suggested (selected) recipe A is shown in process 400 (FIG. 15). Theselected recipe A is then used to perform a CMP process on a physicalwafer, in which the GDS A is implemented. A measurement is thenperformed on the wafer to determine the hotspots and the topologyinformation on the wafer, and generate wafer results (process 506). Inthe measurement, the positions on the wafer, which positions are markedin process 314 (FIG. 13) will be inspected to determine whether therespective hotspots found in process 312 (FIG. 13) have been eliminatedor at least reduced. If the hotspots are eliminated or at least reduced,it is determined that the recipe is beneficial, and may or may not befurther improved. If the hotspots are not reduced or even worsened, adifferent recipe is needed.

Depending on the wafer results, training data 508 (which may include themeasurement results) are fed back to the hotspot prevention model 223,and the hotspot prevention model 223 is updated (process 510). Forexample, when the measurement results indicate that some new hotspotsare found, or some expected hotspots do not exist, the hotspotprevention model is updated so that the hotspot prevention model willoutput the newly found hotspots, and will no longer output the modelthat do not exist.

Furthermore, based on the measurement results, recipe A may be revised,for example, for eliminating the remaining found hotspots. Another wafermay then be manufactured and a CMP is performed using the revised recipeA, and measurement may be performed to determine the hotspot andtopology information. This constructs an iteration, and the iterationmay be continued until the results are satisfactory.

GDS A, B, C, D may be identical to each other, and the initial recipesA, B, C, D may be chosen to be different, so that recipes may beimproved in different directions, and eventually, an optimized recipemay be selected among the revised recipes A, B, C, and D, each beingrevised in their own iterations. GDS A, B, C, D that are different fromeach other may also be used, so that the resulting models may coverdifferent layouts, and more recipes may be generated for differentlayouts.

FIG. 19 schematically illustrates a tool 600 for performing the tasks asaforementioned, which include and are not limited to calculation,determination, and storing hotspot library 222. For example, theprocesses as shown in process flows 200, 300, 400, and 500 may all beperformed using the computer (processors) 602, which includes thehardware and the software (computer program codes). The program codes oftool 600 may be embodied on a non-transitory storage media, such as ahard drive, a disc, or the like. Hotspot library 222, which may beembodied in a storage such as hard disc, is electrically and signallyconnected to the computer 602 for saving and retrieving.

The embodiments of the present disclosure have some advantageousfeatures. By predicting hotspots and selecting recipes toreduce/eliminate the predicted hotspots, there is no need to manufacturephysical wafer and measuring the physical wafer to find hotspots. Thefirst physical wafer may be manufactured using the recipe that hopefullywill eliminate potential hotspots. The manufacturing cycles may bereduced significantly, for example, by a third of time.

In accordance with some embodiments of the present disclosure, a methodcomprises cropping a plurality of images from a layout of an integratedcircuit; generating a first plurality of hash values, each from one ofthe plurality of images; loading a second plurality of hash valuesstored in a hotspot library; comparing each of the first plurality ofhash values with each of the second plurality of hash values, whereinthe comparing comprises calculating a similarity value between the eachof the first plurality of hash values and the each of the secondplurality of hash values; comparing the similarity value with apre-determined threshold similarity value; and in response to a resultthat the similarity value is greater than the pre-determined thresholdsimilarity value, recording a position of a corresponding image that hasthe result, wherein the position is the position of the correspondingimage in the layout. In an embodiment, the plurality of images form anarray, and the position comprises a row number and a column number ofthe corresponding image in the array. In an embodiment, the methodfurther comprises manufacturing the integrated circuit on a wafer,wherein the manufacturing comprises performing a chemical mechanicalpolish process on the wafer; and finding hotspots from the position,wherein the hotspots are defects in the wafer as a result of thechemical mechanical polish process. In an embodiment, the cropping theplurality of images comprises dividing the layout into an array ofimages, and cropping each of the plurality of images in the array. In anembodiment, the pre-determined threshold similarity value is 0.9. In anembodiment, the method further comprises cropping an additionalplurality of images from an additional layout of an additionalintegrated circuit; generating a third plurality of hash values, eachfrom one of the additional plurality of images; comparing each of thethird plurality of hash values with all hash values stored in thehotspot library to find a group of hash values similar to the thirdplurality of hash values; and ranking similarity values of the group ofhash values. In an embodiment, the method further comprises selecting arecipe associated with one of the group of hash values.

In accordance with some embodiments of the present disclosure, a methodcomprises cropping a plurality of images from a layout of an integratedcircuit; generating a plurality of hash values, each from one of theplurality of images; searching from a hotspot library to find similarhash values that are similar to the plurality of hash values, whereinthe hotspot library stores hash values indexed to images that havehotspots; and marking positions of some of the plurality of images thatare associated with the similar hash values on the layout of theintegrated circuit. In an embodiment, the method further comprisesimplementing the layout of the integrated circuit on a wafer, whereinthe implementing comprises performing a chemical mechanical polishprocess on the wafer using a recipe; and inspecting the positions on thewafer to determine hotspots at the positions. In an embodiment, themethod further comprises determining the recipe based on the similarhash values that have been found. In an embodiment, the similar hashvalues are associated with a plurality of recipes in the hotspotlibrary, and wherein the recipe is selected from the plurality ofrecipes. In an embodiment, the recipe comprises a first duration and aflow rate of a slurry used in the chemical mechanical polish process,and a second duration and a magnitude of each of a dressing and a downforce used in the chemical mechanical polish process. In an embodiment,each of the plurality of images has a square shape, with a length and awidth of the square shape being in a range between about 64 μm and about256 μm. In an embodiment, the hotspot library comprises a plurality ofentries, each comprising a hash value, an image, a recipe, and atopology information. In an embodiment, the hotspot library is indexedby hash values.

In accordance with some embodiments of the present disclosure, a systemcomprises a library stored in a tangible media, the library comprising aplurality of entries, each comprising a hash value; an image associatedwith the hash value, wherein the image comprises a hotspot; a recipeconfigured to reduce the hotspot; and a topology information of thehotspot. In an embodiment, the system further comprises a toolcomprising a software, wherein the software is configured to generatethe hash value from the image. In an embodiment, similarity values ofany pair of hash values in the plurality of entries stored in thelibrary is smaller than about 0.9. In an embodiment, the hotspotcomprises a recess or a bump occurring at a center of the image. In anembodiment, the recipe comprises process conditions that are configuredto reduce the hotspot.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

1. A method comprising: cropping a plurality of images from a layout ofan integrated circuit; generating a first plurality of hash values, eachfrom one of the plurality of images, wherein each of the first pluralityof hash values comprises a series of digits and letters; loading asecond plurality of hash values stored in a hotspot library; comparingeach of the first plurality of hash values with each of the secondplurality of hash values, wherein the comparing comprises calculating asimilarity value between the each of the first plurality of hash valuesand the each of the second plurality of hash values; comparing thesimilarity value with a pre-determined threshold similarity value; andin response to a result that the similarity value is greater than thepre-determined threshold similarity value, recording a position of acorresponding image that has the result, wherein the position is theposition of the corresponding image in the layout.
 2. The method ofclaim 1, wherein the plurality of images form an array, and the positioncomprises a row number and a column number of the corresponding image inthe array.
 3. The method of claim 1 further comprising: manufacturingthe integrated circuit on a wafer, wherein the manufacturing comprisesperforming a chemical mechanical polish process on the wafer; andfinding hotspots from the position, wherein the hotspots are defects inthe wafer as a result of the chemical mechanical polish process.
 4. Themethod of claim 1, wherein the cropping the plurality of imagescomprises dividing the layout into an array of images, and cropping eachof the plurality of images in the array.
 5. The method of claim 1,wherein the pre-determined threshold similarity value is 0.9.
 6. Themethod of claim 1 further comprising: cropping an additional pluralityof images from an additional layout of an additional integrated circuit;generating a third plurality of hash values, each from one of theadditional plurality of images; comparing each of the third plurality ofhash values with all hash values stored in the hotspot library to find agroup of hash values similar to the third plurality of hash values; andranking similarity values of the group of hash values.
 7. The method ofclaim 6 further comprising selecting a recipe associated with one of thegroup of hash values.
 8. A method comprising: cropping a plurality ofimages from a layout of an integrated circuit, wherein the croppingcomprises: dividing the layout of the integrated circuit into an arrayof rectangular regions, each being one of the plurality of images;generating a plurality of hash values, each from one of the array ofrectangular regions; searching from a hotspot library to find similarhash values that are similar to the plurality of hash values, whereinthe hotspot library stores hash values indexed to images that havehotspots; and marking positions of some of the plurality of images thatare associated with the similar hash values on the layout of theintegrated circuit.
 9. The method of claim 8 further comprising:implementing the layout of the integrated circuit on a wafer, whereinthe implementing comprises performing a chemical mechanical polishprocess on the wafer using a recipe; and inspecting the positions on thewafer to determine hotspots at the positions.
 10. The method of claim 9further comprising determining the recipe based on the similar hashvalues that have been found.
 11. The method of claim 10, wherein thesimilar hash values are associated with a plurality of recipes in thehotspot library, and wherein the recipe is selected from the pluralityof recipes.
 12. The method of claim 9, wherein the recipe comprises afirst duration and a flow rate of a slurry used in the chemicalmechanical polish process, and a second duration and a magnitude of eachof a dressing and a down force used in the chemical mechanical polishprocess.
 13. The method of claim 8, wherein neighboring ones of thearray of rectangular regions are in physical contact with each other.14. The method of claim 8, wherein the hotspot library comprises aplurality of entries, each comprises a hash value, an image, a recipe,and a topology information.
 15. The method of claim 14, wherein thehotspot library is indexed by hash values.
 16. A system comprising: alibrary stored in a tangible media, the library comprising a pluralityof entries, each comprising: a hash value, wherein the hash valuecomprises a series of digits and letters; an image associated with thehash value, wherein the image comprises a hotspot; a recipe configuredto reduce the hotspot; and a topology information of the hotspot. 17.The system of claim 16 further comprising a tool comprising a software,wherein the software is configured to generate the hash value from theimage.
 18. The system of claim 16, wherein similarity values of any pairof hash values in the plurality of entries stored in the library issmaller than about 0.9.
 19. The system of claim 16, wherein the hotspotcomprises a recess or a bump occurring at a center of the image.
 20. Thesystem of claim 16, wherein the recipe comprises process conditions thatare configured to reduce the hotspot.